Methodology of wavelet packet selection for event detection
Résumé
Uterine EMG is very useful for pregnancy and parturition monitoring but its analysis requires efficient tools for detection and identification of events contained in the recordings. The present work is based on the use of wavelet packet (WP) decomposition and direct use of WP coefficients (after delay correction) for change detection. The first step is to select the only WP of the decomposition tree that are able to highlight changes in the recordings using a training set of signals. In this way, one of the main contributions of the current work is to propose and test a criterion based on a model of the distribution of the estimated Kullback–Leibler distance. As WP decomposition produces a redundant tree, a second step proposes a best basis selection based on the suppression of WP without any specificity in terms of change detection. Results evidenced the efficiency of the method for simulated signals (detection probability >95%, false alarm <6%) as well as for real uterine EMG recordings (detection probability >98%, false alarm <6%). Redundancy reduction suppressed half the number of WP selected in the first selection step without any degradation of the overall detection performance. Any application where events to be detected are characterized by their frequency content is a good candidate for such a methodology.